Automatic system for controlling appliances

ABSTRACT

A system for managing house appliances supplied through a power grid is provided. Each house appliance operates according to at least one corresponding operative mode. Each operative mode comprises a sequence of operative phases. The system comprises at least one control unit interfaced with the house appliances for exchanging data. The at least one control unit collects power profile data comprising timing and electric power consumption data of each operative phase of each operative mode of the house appliances; generates a time schedule of the house appliances operations by distributing in time the execution of the operative phases thereof such that total power consumption of house appliances is kept under a maximum power threshold of the power grid; and controls the operation of the appliances based on the time schedule. The at least one control unit is configured to generate the time schedule by exploiting a Particle Swarm Optimization approach.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a system and a method for monitoringand controlling the electric power consumption of home appliancessupplied by the line power.

2. Description of the Related Art

Nowadays, the management of the domestic electric power consumption isbecome a very complex issue.

Indeed, domestic electric power consumption is highly variable.Typically, during a working day, highest electric power consumptionsmainly occur during morning and evening, while during weekends electricpower consumption is more evenly distributed. Other factors may stronglyinfluence the domestic power consumption, such as for example theholidays and seasons of the year. Moreover, if on one hand the domesticelectric power demand has reached higher and higher peak levels, on theother hand environmental and economical reasons have brought to thediffusion of residential renewable electric power generation units,whose electric power generation capabilities fluctuates based on theweather conditions. The complexity of this scenario is further increasedby the introduction of time-varying and dynamic electric power tariffs.

In order to efficiently manage the domestic electric power consumptionso as to avoid overloads (i.e., to avoid that the maximum powerthreshold of the power grid is exceeded) and reduce the costs, all theabovementioned aspects should be taken into account. Since this is avery complex task, the introduction of a system capable of automaticallymanaging the domestic power consumptions is strongly desirable.

International patent application WO2009/097400 discloses a system and amethod for monitoring and controlling the power consumption of apower-consuming device. The system and method may connect to a powersource and a power-consuming device, connecting the power-consumingdevice to the power source. The power usage of the power-consumingdevice may then be measured and monitored. This monitoring data may thenbe stored and optionally sent to a controlling device on a data network.The location of the power-consuming device may also be determined,recorded, and sent to a controlling device. The system may also controlthe power usage of the power-consuming device. In some cases, a remoteserver may connect multiple energy monitoring systems in order to gainadditional efficiencies and foster a community-based social network.

In the paper “Scheduling energy consumption with local renewablemicro-generation and dynamic electricity prices” by Onur Derin andAlberto Ferrante, GREEMBED 2010, a scheduling problem for householdtasks is discussed, directed to help users save money spent on theirenergy consumption. A system model is presented, which relies onelectricity price signals, availability of locally-generated power andflexible tasks with deadlines. A case study shows that cost savings arepossible but fast and efficient solutions to the scheduling problem areneeded for their real world use.

SUMMARY OF THE INVENTION

The Applicant has found that the above mentioned solutions known in theart are affected by drawbacks, and/or are not efficient.

The system disclosed in patent application WO2009/097400 does notprovide for automatically optimizing the domestic electric powerconsumption. Moreover, the system of WO2009/097400 is not designed toprevent overloads.

The optimization method disclosed in the paper by Onur Derin and AlbertoFerrante is based on a brute-force approach, which involves theprocessing of a very large amount of data. Therefore, this method has tobe carried out by hardware units provided with high computationalresources. In any case, a method based on a brute-force approach such asthe one proposed by Onur Derin and Alberto Ferrante requires a highamount of time to provide satisfactory results, and thus it is scarcelysuitable to be used in dynamic scenarios, such as the management ofdomestic electric power consumption.

In view of the state of the art outlined in the foregoing, the Applicanthas faced the problem of how to provide a system and a method formanaging house appliances which is capable of automatically optimizingthe domestic power consumption and preventing the occurrences ofoverloads in a relatively short time, without requiring highcomputational resources.

An aspect of the present invention relates to a system for managinghouse appliances supplied through a power grid. Each house appliance isadapted to operate according to at least one corresponding operativemode. Each operative mode comprises a sequence of operative phases. Thesystem comprises at least one control unit interfaced with the houseappliances for exchanging data. The at least one control unit isconfigured to: collect power profile data comprising timing and electricpower consumption data of each operative phase of each operative mode ofthe house appliances; generate a time schedule of the house appliancesoperations by distributing in time the execution of the operative phasesthereof in such a way that at any time the total power consumption ofthe house appliances is kept under a maximum power threshold of thepower grid; and control the operation of the appliances based on thetime schedule. The at least one control unit is configured to generatethe time schedule by exploiting a Particle Swarm Optimization approachbased on a nature-inspired evolutionary flight.

According to an embodiment of the present invention, the at least onecontrol unit is configured to generate the time schedule based on aQuantum Particle Swarm Optimization approach exploiting a Lévyprobability distribution.

According to an embodiment of the present invention, the at least onecontrol unit is configured to generate the time schedule by: a) randomlygenerating a population of candidate time schedules within a searchspace comprising all the possible candidate time schedules; b) for eachcandidate time schedule of the population, calculating a correspondingcost by means of a cost function; c) moving the candidate time schedulesin the search space based on the cost thereof with a Quantum ParticleSwarm Optimization approach exploiting a Lévy probability distribution;and d) generating the time schedule based on the candidate timeschedules.

According to an embodiment of the present invention, the at least onecontrol unit is configured to select a candidate time schedule globalbest position in the search space among positions previously taken byall the candidate time schedules in the search space based on the costthereof. For each candidate time schedule, the at least one control unitis configured to move the candidate time schedule by selecting acandidate time schedule personal best position in the search space amongpositions previously taken by such candidate time schedule in the searchspace based on the cost thereof, and moving the candidate time schedulebased on the candidate time schedule global best position and based onits candidate time schedule personal best position.

According to an embodiment of the present invention, the at least onecontrol unit is configured to move the candidate time schedule from acurrent position in the search space to a new position in the searchspace by calculating a personal attractor position in the search spacebased on the candidate time schedule personal best position and based onthe candidate time schedule global best position, and setting the newposition in the search space based on the personal attractor positionand based on the difference between: a) the personal attractor positionand b) the current position, multiplied by a random number with Lévydistribution.

According to an embodiment of the invention, the cost function of acandidate time schedule depends on the difference between the maximumpower threshold of the power grid and a power consumption of the houseappliances that would occur if the house appliances operated followingthe candidate time schedule.

According to an embodiment of the invention, the cost function of acandidate time schedule further depends on user preferences and/or onconstraints imposed by the operative modes of the house appliances.

According to an embodiment of the invention, the at least one controlunit is further configured to control the house appliances in order toreduce the power consumption of the currently active house appliancesand/or to deactivate at least one of the currently active houseappliances if the power consumption of the currently active houseappliances is higher than a guard threshold lower than the maximum powerthreshold of the power grid.

According to an embodiment of the present invention, the at least onecontrol unit is configured to sequentially drive the currently activehouse appliance to reduce the power consumption thereof and/or tosequentially deactivate them according to a priority list defined by theuser and/or starting from the currently active house appliancesconsuming more power, until the power consumption of the currentlyactive house appliances falls below the guard threshold.

According to an embodiment of the present invention, the houseappliances are located in a domestic area, and the at least one controlunit comprises a local control unit located in the domestic areawirelessly interfaced with the house appliance for exchanging data.

According to an embodiment of the present invention, the at least onecontrol unit further comprises at least one remote control unit remotelyconnected with the local control unit. The at least one remote controlunit is configured to generate at least one remotely generated timeschedule, and the local control unit is configured to generate a locallygenerated time schedule. The time schedule is the selected between theat least one remotely generated time schedule and the locally generatedtime schedule based on the costs of the at least one remotely generatedtime schedule and of the locally generated time schedule.

Another aspect of the present invention relates to a method for managinghouse appliances supplied through a power grid. Each house appliance isadapted to operate according to at least one corresponding operativemode; each operative mode comprises a sequence of operative phases. Themethod includes collecting power profile data comprising timing andelectric power consumption data of each operative phase of eachoperative mode of the house appliances; generating a time schedule ofthe house appliances operations by distributing in time the execution ofthe operative phases thereof in such a way that at any time the totalpower consumption of the house appliances is kept under a maximum powerthreshold of the power grid; and controlling the operation of theappliances based on the time schedule. The step of generating the timeschedule includes exploiting a Particle Swarm Optimization approachbased on a nature-inspired evolutionary flight.

According to an embodiment of the present invention, the step ofgenerating the time schedule is based on a Quantum Particle SwarmOptimization approach exploiting a Lévy probability distribution.

According to an embodiment of the present invention, the generating thetime schedule includes: a) randomly generating a population of candidatetime schedules within a search space comprising all the possiblecandidate time schedules; b) for each candidate time schedule of thepopulation, calculating a corresponding cost by means of a costfunction; c) moving the candidate time schedules in the search spacebased on the cost thereof with a Quantum Particle Swarm Optimizationapproach exploiting a Lévy probability distribution, and d) generatingthe time schedule based on the candidate time schedules.

According to an embodiment of the present invention, the method furthercomprises selecting a candidate time schedule global best position inthe search space among positions previously taken by all the candidatetime schedules in the search space based on the cost thereof, and, foreach candidate time schedule, moving the candidate time schedule byselecting a candidate time schedule personal best position in the searchspace among positions previously taken by such candidate time schedulein the search space based on the cost thereof, and moving the candidatetime schedule based on the candidate time schedule global best positionand based on its candidate time schedule personal best position.

According to an embodiment of the present invention, the moving thecandidate time schedule from a current position in the search space to anew position in the search space includes calculating a personalattractor position in the search space based on the candidate timeschedule personal best position and based on the candidate time scheduleglobal best position, and setting the new position in the search spacebased on the personal attractor position and based on the differencebetween: a) the personal attractor position and b) the current position,multiplied by a random number with Lévy distribution.

A further aspect of the present invention relates to a computer program.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the present invention will bemade evident by the following description of some exemplary andnon-limitative embodiments thereof, to be read in conjunction with theattached drawings, wherein:

FIG. 1A illustrates a system for monitoring and controlling houseappliances according to an embodiment of the present invention;

FIG. 1B illustrates an example of a time schedule of the houseappliances generated by the system of FIG. 1A according to an embodimentof the present invention;

FIG. 2 illustrates in terms of functional blocks the main operations ofa power managing procedure carried out by a local control unit of thesystem according to an embodiment of the present invention;

FIG. 3 illustrates in terms of functional blocks a time scheduleupdating sub-procedure of the power managing procedure of FIG. 2according to an embodiment of the present invention;

FIG. 4 illustrates in terms of functional blocks an optimizationprocedure of the time schedule updating sub-procedure of FIG. 3according to an embodiment of the present invention;

FIG. 5 illustrates in terms of functional blocks a swarm flightprocedure according to an embodiment of the present invention; and

FIG. 6 illustrates in terms of functional blocks a reactivesub-procedure of the power managing procedure of FIG. 2 according to anembodiment of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS OF THE INVENTION

With reference to the drawings, FIG. 1A illustrates a system 100 formonitoring and controlling house appliances 110(i) (i=1 to N) which aresupplied by the line power provided by the power grid in a domestic area120, such as a house, according to an embodiment of the presentinvention. A local control unit 130 is located in or in the proximity ofthe domestic area 120 for managing the operation of the system 100 bycarrying out a power managing procedure which will be described indetail in the following of the description. For this purpose, the localcontrol unit 130 is wirelessly interfaced with each house appliance110(i) for receiving therefrom data regarding its power consumption, andfor correspondingly transmitting thereto commands for managing itsoperation.

The local control unit 130 may be located in a hardware processing unit,such as a domestic wi-fi station (e.g., an ADSL router), and the powermanaging procedure may be carried out by running an application and/or asoftware program locally stored in a memory of such processing unitand/or remotely available, for example through the Internet.

Similar considerations apply if different connections (wired and/orwireless) are exploited. Moreover, the local control unit 130 may haveanother structure, such as a dedicated and/or stand-alone hardware unit,or may comprise similar elements, such as cache memories temporarilystoring the software program implementing the managing procedure.

Each house appliance 110(i) may be a smart appliance, i.e., an applianceequipped with a communication interface adapted to exchange data withthe local control unit 130, such as a wi-fi interface, a ZigBeeinterface, or a power-line interface, as well as with the capability ofproviding the local control unit 130 with the power profiles of itsoperative modes, and of being controlled in response to commandsreceived by the local control unit 130. In the present document, theoperative modes of an appliance are to be intended as the possible wayssuch appliance may operate. The complexity and the number of theoperative modes of an appliance strongly vary based on the appliancetype. For example, while an electric water heating typically has onlyone single operative mode, which provides for alternatively turning onand off the water heating resistance, a washing machine typicallyincludes a wide range of different operative modes, each onecorresponding to a particular washing program (e.g., cotton washing,wool washing and so on) with a particular set of input parameters (suchas water temperature, presence or not of spin-drying and so on). Thepower consumption of each operative mode of the generic appliance ismodeled by a corresponding power profile including a set of sequentialenergy phases, each one corresponding to a respective phase of theoperative mode. For example, in a washing machine program, a phase maycorrespond to the water loading, another one to the drum spinning, andso on. Each energy phase is characterized by several parameters, such asthe maximum power peak used in the phase, the time duration of the phaseand the maximum allowable delay of the beginning of that phase after theend of the previous energy phase. By transmitting the power profile ofthe operative mode of an appliance to the local control unit 130, thelatter is thus capable of estimating the power consumption of suchappliance.

More traditional (i.e., non-smart) appliances, unsupplied withcommunication interfaces adapted to exchange data with the local controlunit 130, and/or lacking of the capability of providing the localcontrol unit 130 with the power profiles of its operative modes and/orof being remotely controlled, are connected to the power grid forreceiving the line power through so-called smart plugs 135. Briefly, asmart plug is equipped with a measuring unit capable of operating powermeasurements on the appliance which are connected to a communicationunit adapted to exchange data with the local control unit 130 and arelay unit adapted to control the appliance supply in response tocommands received by the local control unit 130. Thanks to the presenceof the smart plugs 135, the local control unit 130 is able to obtaininformation relating to the power usage of such appliances, and deducingan approximate power profile to be exploited by the power managingprocedure.

As will be described in detail in the following of the presentdescription, according to an embodiment of the present invention, thepower managing procedure may be also carried out by a remote controlunit 140 which is remotely interfaced with the local control unit 130,for example through a remote network 150, such as the Internet. In thiscase as well, the power managing procedure may be carried out by runningan application stored in a memory of the remote controller unit and/orremotely available, for example through the Internet. The remote controlunit 140 may be further configured to collect data sent from theappliances 110(i) and store them into proper databases (not shown) inorder to make them available by further remote control units.

The power managing procedure according to an embodiment of the presentinvention provides for scheduling the operation of the various houseappliances 110(i) of the domestic area 120 in such a way to efficientlymanage the domestic electric power consumption, avoiding the occurrenceof overloads and reducing the overall cost by concentrating as much aspossible the operations of the house appliances 110(i) in the hours ofthe day wherein the electric power cost is cheapest, respecting at thesame time constraints imposed by the operation of the house appliances110(i) and the time preferences set by the user. For this purpose, thepower managing procedure carried out by the local control unit 130 (andpossibly by one or more remote control units 140) is directed togenerate a time schedule—identified in figure with reference 160—of thehouse appliances 110(i) operations optimized so as to accomplish theabovementioned objectives. As will be described in detail in thefollowing of the present description, the time schedule 160 is generatedby properly scheduling the start time of all energy phases of the powerprofiles of the appliances 110(i), taking into account time and powerconstraints. The power managing procedure further provides fordynamically and rapidly updating the time schedule 160 in response toany possible unforeseeable or disruptive event, such as the directrequest of activating a specific house appliance 110(i) by an user, thevariation of the electric power cost due to a dynamic variation of thedaily tariff profile, a possible imminent overload occurrence, thevariation of electric power amount generated by photovoltaic solarpanels as a result of cloud cover change and so on, so as to adapt thedomestic electric power consumption in order to avoid the occurrences ofoverloads and reduce the costs.

In order to illustrate how a possible time schedule 160 is structuredaccording to an embodiment of the present invention, reference is madeto the example of FIG. 1B. In this example, there is a first time slotH1 (e.g., the time slot corresponding to the office hours of a day)corresponding to a first electric power cost and a second time slot H2(e.g., the time slot corresponding to the remaining hours of the day)corresponding to a second electric power cost lower than the first one.Moreover, in this very simplified example only two appliances 110(i) areconsidered, and namely:

-   -   an electric water heater, having a single power profile 165        comprising an alternating repetition of a first energy phase        P′1, corresponding to the situation in which its heating        resistance is on, and a second energy phase P′2, corresponding        to the situation in which its heating resistance is off, and    -   a washing machine, having a single power profile 170 comprising        an ordered sequence of three energy phases: a first energy phase        P″1 corresponding to a water loading phase, a second energy        phase P″2 corresponding to a water heating phase, and a third        energy phase P″3 corresponding to a drum rotation phase.

The exemplary time schedule 160 is generated by distributing in time theenergy phases of the power profiles 160 and 170 in such a way that atany time the total power consumption is kept under the maximum powerthreshold TH of the power grid, so as to avoid any overload occurrence,and that the highest power consumptions preferably occur in the cheapersecond time slot H2. The need of avoiding overload occurrences is one ofpossible power constraints affecting a scheduling, while the preferenceof concentrating the highest power consumptions in the cheapest hours ofthe day is one of possible time constraints affecting a scheduling. Inthe considered exemplary time schedule 160, during a first period it isscheduled the execution of the phase P′2, during a second period theconcurrent execution of the energy phases P′1 and P″1, during a thirdperiod the concurrent execution of the energy phases P′2 and P″2, duringa fourth period the execution of the energy phase P′1, and during afifth period the concurrent execution of the energy phases P′2 and P″3.A further example of time constraint is given by the need of schedulingenergy phase P″2 after energy phase P″1 and energy phase P″3 afterenergy phase P″2, in order to allow the washing machine to correctlyoperate. Moreover, while energy phase P″2 has been scheduled right awayafter energy phase P″1, in the exemplary schedule 160 illustrated inFIG. 1B a time interval occurs between the end of energy phase P″2 andthe start of energy phase P″3. Since energy phase P″2 corresponds to awater heating phase, a still further time constraints might provide forthe execution of the energy phase P″3 that is sufficiently close to theend of phase P″2, in order to avoid that water cools down to an extentsuch to compromise the washing machine operation efficiency.

In real actual cases, the appliances 110(i) located in the domestic area120 are numerous, and the power profiles thereof includes a high numberof energy phases, linked together by complicated time constrains.Consequently, generating optimized time schedules 160 is a very complextask, which is difficult to be accomplished. When the number ofvariables at stake is very high, it is nearly impossible to obtain the“overall best” time schedule in almost real time and without highcomputation capabilities (such as in the case of a system to beinstalled in a domestic area). Indeed, the generation of optimized timeschedule can be classified as a “Resource Constrained SchedulingProblem”, which is known as being an NP-hard combinatorial optimizationproblem. The system 100 according to the embodiments of the presentinvention is instead directed to calculate in relatively short timesoptimized time schedules 160 which have the quality of ones among thebest time schedules, without the need of high computation capabilities.The quality of a time schedule 160 may be quantified by a correspondingcost function formed by a sum of penalty components each one measuringhow a respective one of the abovementioned constraints has been violatedby running the appliances 110(i) according to the time schedule 160. Thebetter a constraint is respected, the lower its corresponding penaltycomponent. Therefore, with the same conditions, a time schedule having acorresponding cost function value is considered to have a higher qualitythan other time schedules having higher cost function values.

FIG. 2 illustrates in terms of functional blocks the main operations ofthe power managing procedure (identified in figure with reference 200)which is adapted to be carried out by the local central unit 130 forcontrolling the appliances 110(i) of the system 100 according to anembodiment of the present invention.

The local central unit 130 is configured to be constantly active,remaining in a waiting state ready to respond to any possible eventrequiring a new scheduling (block 210). Such event may be a directrequest of activating a specific house appliance 110(i) by the user, theexpiration of a predetermined time period, or a generic conditionvariation, such as an unplanned overload occurrence.

In response to such events, the local central unit 130 performs a timeschedule updating sub-procedure (block 220), whose output is an updatedversion (i.e., which takes into account the event occurrence) of thetime schedule 160. The appliances 110(i) of the system 100 are theninformed of the changes carried out in the time schedule 160, so as toact accordingly. For this purpose, each appliance 110(i) is providedwith data relating the portions of the updated time schedule 160 whichconcern the execution of energy phases of the power profile of suchappliance 110(i). In case the appliance 110(i) is a smart appliance, itautomatically adapts and synchronizes its operation based on thereceived data. If instead the appliance 110(i) is a standard “non-smart”appliance, connected to the power grid through a smart plug 135, therelay unit of the smart plug 135 is configured to control the appliancesupply based on the received data.

After the time schedule updating procedure is carried out, the overallelectric power actually drained by the presently active appliances110(i) is measured (block 230), for example by a smart meter (notillustrated in the figures).

If the measured power is lower than a guard threshold GTH, which isadvantageously set sufficiently lower than the maximum power thresholdTH of the power grid, the local control unit 130 returns to its waitingstate (exit branch Y of block 240, returning back to block 210).

If the measured power is instead higher than the guard threshold GTH, itmeans that the scheduling defined by the updated time schedule 160 isrequiring a dangerously high electric power demand, which is too closeto the maximum power threshold TH. In this case (exit branch N of block240), the power managing procedure provides for carrying out a reactivesub-procedure (block 250) directed to reduce the power consumptionand/or deactivate one or more of the currently active appliances 110(i)so as to brought the measured drained power under the guard threshold.After the execution of the reactive sub-procedure, the local controlunit 130 returns to its waiting state.

FIG. 3 illustrates in terms of functional blocks the time scheduleupdating sub-procedure 220 according to an embodiment of the presentinvention.

As already mentioned above, one or more remote control units 140 may beinterfaced with the local control unit 130, for example through theInternet. According to an embodiment of the present invention, the timeschedule updating sub-procedure 220 may be carried out by the localcontrol unit 130 only (exit branch N of block 310), as well as both bythe local control unit 130 and by the remote control unit(s) 140 (exitbranch Y of block 310).

Making reference in particular to the first case (operations performedon local control unit 130 side only), when the user sends a request ofactivating a specific appliance 110(i), according to an embodiment ofthe present invention the power profile of such appliance 110(i) istransmitted to the local control unit 130 (block 320). According toanother embodiment of the invention, instead of transmitting the powerprofile of an appliance 110(i) every time the appliance 110(i) isrequested to be activated by a user, the power profiles of all theappliances 110(i) are already stored in a local or remote database (notillustrated), which can be accessed by the local control unit 130. Inany case, if the event which has triggered the time schedule updatingsub-procedure 220 is a time period expiration or a condition variation,the local control unit 130 does not require any new power profile, andthe operations of block 320 are skipped. At this point, as will bedescribed in greater detail in the following of the description, thelocal control unit 130 executes an optimization procedure to update theselected time schedule 160 (block 330) taking into account also thenewly received data (if present). It has to be noted that the first timethe optimization algorithm is carried out, e.g., the first time a userrequests the activation of an appliance 110(i), or in case the eventhaving triggered the time schedule updating sub-procedure 220 hasoccurred after that all possible previously scheduled applianceoperations have already terminated, the time schedule 160 to be updatedwill be a blank time schedule.

Making reference to the second case (operations concurrently performedon local control unit 130 and remote control unit 140), the powerprofile of the appliance 110(i) that is requested to be activated istransmitted to the local control unit 130 (block 340). As in theprevious case, according to an embodiment of the present invention, thelocal control unit 130 may retrieve the power profile by accessing aproper database. Again, if the event which has triggered the timeschedule updating sub-procedure 220 is a time period expiration or acondition variation, the local control unit 130 does not require any newpower profile, and the operations of block 340 are skipped. The samepower profile of the appliance 110(i) that is requested to be activatedis then transmitted to the remote control unit 140 (block 350). At thispoint, both the local control unit 130 and the remote control unit 140independently execute an optimization procedure to update the selectedtime schedule 160 (block 360). Such optimization procedure is equal tothe one performed at block 330. Since the local control unit 130 and theremote control unit 140 run the optimization algorithm independently ofeach other, the updated time schedule 160′ generated by the former willgenerally differ from the updated time schedule 160″ generated by thelatter. The updated time schedule 160″ generated by the remote controlunit 140 is then transmitted to the local control unit 130 (block 370).The cost functions of the two time schedules 160′, 160″ are thencompared to each other, and the selected time schedule 160 will be theone having the lowest cost function (block 380).

According to an embodiment of the present invention, the optimizationprocedure 330 (and, therefore, the optimization procedure 360) is basedon a Particle Swarm Optimization (PSO) approach. The PSO approach is apopulation-based metaheuristic algorithm, which has been inspired byswarm intelligence of fish and birds. Briefly, the optimizationprocedure 330 provides for randomly generating a population, hencereferred to as “swarm”, of candidate solutions (i.e., candidate timeschedules), referred to as “particles of the swarm”, and then movingthese particles in the search-space comprising all the possible timeschedules. Each particle's movement is influenced by its personal bestposition in the search-space (i.e., the position corresponding to thelowest cost function value among the positions thus far found by thatparticle) as well as by the global best position in the search-space(i.e., the position corresponding to the lowest cost function valueamong all positions thus far found by all the particles in the swarm).This combined knowledge will move the swarm of particles toward the bestsolutions.

According to the PSO approach, each individual particle is identified bya position vector and by a corresponding velocity vector. The PSOapproach is iterative, wherein the position vector and the velocityvector of each particle are updated at each iteration. A possibleexample of PSO approach provides for updating the position vector andthe velocity vector in the following way:

the velocity vector is updated by weighting the velocity vector of theprevious iteration by an inertia parameter, and by summing the weightedvelocity vector of the previous iteration with:

-   -   a) a first velocity array component, which is directed towards        the particle personal best position and has a magnitude        proportional to a first random number, for example uniformly        distributed in [0,1], and    -   b) a second velocity array component, which is directed toward        the global best position and has a magnitude proportional to a        second random number, for example uniformly distributed in        [0,1],

the position vector is updated by summing the position vector of theprevious iteration with the updated velocity vector.

Therefore, each particle in the swarm modifies its position with avelocity that includes a first velocity component that attracts theparticle towards the best position so far achieved by the particleitself, and a second velocity component that attracts the particletoward the best solution so far achieved by the swarm as a whole. Thefirst velocity component represents the “personal experience” of theparticle, while the second velocity component represents the “socialcommunication skill” of the particles.

An example of a PSO approach implementation is disclosed in ParticleSwarm Optimization by Kennedy, J. and Eberhart, R. in Proc. Of the IEEEInt. Conf. On Neural Networks, Piscataway, NK, pages 1942-1948.

According to an embodiment of the present invention, the optimizationprocedure 330 is based on a Quantum Particle Swarm Optimization (QPSO)approach, i.e., a PSO approach in which the particles of the swarm aremoved in the search-space according to quantum mechanics laws. Inclassical (non-quantum) PSO, particles have a mass and move in thesearch space by following Newtonian dynamics, updating their positionand velocity vectors at each iteration. Conversely, in quantum mechanicthe position and the velocity of a particle cannot be determinedsimultaneously according to the uncertainty principle. In QPSO, thepositions of the particles are determined by a wavefunction, where anattractive potential field will eventually pull all particles to thelocation defined by corresponding local attractors. For example,according to an embodiment of the present invention, the wavefunctionmay be the Schrödinger equation.

Examples of QPSO are disclosed in Particle swarm optimization withparticles having quantum behavior by J. Sun, B. Feng, and W. Xu, IEEECongress on Evolutionary Computation, pp. 325-31, 2004, AdaptiveParameter Selection of Quantum-Behaved Particle Swarm Optimization onGlobal Level, by Wenbo Xu, Jun Sun and Bin Feng, School of InformationTechnology, Southern Yangtze University, Wuxi, Jiangsu, China, MESFET DCmodel parameter extraction using Quantum Particle Swarm Optimization, bySamrat L. Sabat, Leandro dos Santos Coelho, Ajith Abraham,Microelectronics Reliability 49 (2009), A Review of Quantum-behavedParticle Swarm Optimization, Wei Fang, Jun Sun, Yanrui Ding, Xiaojun Wu,Wenbo Xu, School of Information Technology, Jiangnan University, No1800, Lihu Avenue, Wuxi 214 122, China.

According to an embodiment of the present invention, the motion of theparticles (either performed following a PSO or a QPSO approach) isfurther based on a “nature-inspired evolutionary flight,” i.e., itfollows a random walk based on a nature-inspired fractal evolutiontypical of many natural phenomena and higher order animal behavior.

A random walk is a series of consecutive random steps starting from anorigin position. In a random walk, the next position only depends on thecurrent position and on the next step. The next step is calculated basedon a random number drawn from a certain probability distribution. Incase such probability distribution is the Gaussian distribution, therandom walk becomes the Brownian motion.

Various studies have shown that the behavior of many animals and insectsfollows a random walk based on a Lévy probability distribution. Lévyprobability distribution is a probability distribution both stable andheavy-tailed. A stable distribution is such that any sum of n randomnumbers drawn from the distribution is finite and can be expressed as

${{\sum\limits_{i = 1}^{n}\; x_{i}} = {n^{1\text{/}\alpha}x}},$

where α, called “index of stability”, controls the shape of thedistribution probability (0<α≦2). A heavy-tailed distribution has aninfinite variance decaying at large x to λ(x)˜|x|^(−1-α). Due to thestable property, a random walk following the Lévy distribution willcover a finite distance from its original position after any number ofsteps. But also due to the heavy tail (divergence of the variance),extremely long jumps may occur and typical trajectories areself-similar, on all scales showing clusters of shorter stepsinterspersed by long excursions.

Lévy distributions are for example disclosed in Introduction to thetheory of Lévy flights, by A. V. Chechkin, R. Metzler, J. Klafter, andV. Yu. Gonchar.

According to an embodiment of the present invention, the optimizationprocedure 330 is based on a QPSO approach which exploits a Lévyprobability distribution. A Quantum Particle Swarm Optimizationalgorithm with the Lévy probability distribution provides for aparticle's motion circumscribed in the neighborhoods of solutions, butat the same time allowing for extremely long jumps to escape from localoptima.

FIG. 4 illustrates in terms of functional blocks the optimizationprocedure 330 (or the optimization procedure 360) according to anembodiment of the present invention. Although the optimization procedurewill be now described making reference to the local control unit 130only, it has to be appreciated that the phases thereof may be carriedout by the remote control unit 140 as well.

In a first phase of the optimization procedure (block 402), the localcontrol unit 130 creates the swarm by generating M particles P(j) (j=1to M) and positioning them randomly in a search-space X of all thepossible solutions (time schedules) x. In the foregoing the “searchspace” should be construed as the temporal domain in a time period aheadof present time. The number M of particles of the swarm affects both theoutcome and the computational cost of the procedure. A large number ofparticles provide for a broader search, but at the same time updatingtoo many particles may reduce the convergence of the algorithm thatneeds to focus the search in the whereabouts of the most promising spotsso as to find good solutions in relatively short time.

Then, the local control unit 130 selects a particle P(j) of the swarm(block 404) and carries out the following cycle (blocks from 406 to 418)thereon.

In blocks 406 to 410, the local control unit 130 calculates the costfunction F(j)(x) of the solution x corresponding to the current positionof the particle P(j).

For this purpose, the local control unit 130 calculates an overloadpenalty O(x) that measures how much the overload constraint is violatedby using solution x, and then sets the cost function F(j)(x) to w1*O(x),wherein w1 is a weight coefficient, for example on the order of 10⁹(block 406). According to an embodiment of the invention, the overloadpenalty may be equal to 0 for the solutions x in which the overload istotally avoided (i.e., when the total power consumption is kept underthe maximum power threshold TH of the power grid), while it is higherthan 0 for the solutions x corresponding to an overload occurrences(e.g., with the value of O(x) that increases as the total powerconsumption departs from the maximum power threshold TH).

If the overload penalty O(x) is equal to zero, and thus F(j)(x)=0, (exitbranch Y of block 408), meaning that the overload constraint isfulfilled, the local control unit 130 calculates an energy cost penaltyC(x) that measures how much the electric energy would cost using thesolution x based on the daily tariff profile known in advance, and atardiness penalty T(x) that provides a measure of how much the timerequirements provided by the user are fulfilled. The earlier the timeschedule corresponding to a solution x provides for the completion ofthe appliance operations within time requirements provided by the user,the lower the tardiness of such solution. Intuitively, the algorithmgives a better score to solutions that complete earlier rather thanlater. Therefore, the cost function F(j)(x) is set to w2*C(x)+w3*T(x),wherein w2 and w3 are weight coefficients (block 410). For example, w2may be on the order of 1 and w3 may be on the order of 10⁻³.

If instead the overload penalty O(x) is higher than zero, and thusF(j)(x)>0, (exit branch N of block 408), the power cost penalty C(x) andthe tardiness penalty T(x) contributions to the cost function F(j)(x)are neglected due to the large magnitude of the respective weights asdescribed in the foregoing. According to an alternative embodiment ofthe present invention, the contributions of the power cost penalty C(x)and of the tardiness penalty T(x) are instead considered also in casethe overload penalty O(x) is higher than zero.

In any case, the weight coefficient w1 of the overload penalty O(x) isadvantageously set much higher than the weight coefficients w2 and w3 ofthe power cost penalty C(x) and of the tardiness penalty T(x), so as toprovide the overload constraint with the highest priority.

Afterward, the local control unit 130 checks whether the just calculatedcost function F(j)(x) value of the solution x corresponding to thecurrent position of the particle P(j) is lower than the cost functionF(i)(x_(p)) value of the solution x_(p) corresponding to the position ofthe same particle P(j) in a previous swarm arrangement (block 412).

In the affirmative case (exit branch Y of block 412), the solution xcorresponding to the current position of the particle P(j) is elected asthe personal best solution B(j)* so far taken by the particle P(j)(block 414). In the negative case (exit branch N of block 412, going toblock 424), the personal best solution B(j)* so far taken by theparticle P(j) is kept unchanged.

If the current position of the particle P(j) is elected as the personalbest solution B(j)* so far taken by the particle P(j), the local controlunit 130 checks whether the just calculated cost function F(j)(x) valueof the solution x corresponding to the current position of the particleP(j) is also lower than the lowest cost function value (hereinafterreferred to as lowest global cost function value LF) so far taken by anyparticle P(j) of the swarm (block 416).

In the affirmative case (exit branch Y of block 416), the lowest globalcost function value LF is set to the just calculated cost functionF(j)(x) value, and the solution x corresponding to the current positionof the particle P(j) is elected as the global best solution in the swarmBG* (block 418). In the negative case (exit branch N of block 416),block 418 is skipped, and the global best solution so far reached in theswarm BG* is kept unchanged.

The local control unit 130 checks if all the M particles P(j) of theswarm have been subjected to the operations corresponding to blocks from406 to 418 described above.

In the negative case (exit branch N of block 424), the local controlunit 130 selects a new particle P(j) of the swarm (block 426) andrepeats the operations corresponding to blocks from 406 to 418 on thenew particle P(j) (return to block 406).

In the affirmative case (exit branch Y of block 424), the local controlunit 130 checks whether a deadline time DT is expired or not (block 428)from the beginning of the optimization procedure 330.

If the deadline time DT has not yet expired (exit branch N of block428), the swarm arrangement is changed by moving the M particles P(j)thereof in the search-space according to a swarm flight procedure (block430), as will be described later on. Then, the local control unit 130repeats the operations corresponding to blocks from 404 to 426 on allthe M particles P(j) of the swarm in the new arrangement (return toblock 406).

If the deadline time DT has expired (exit branch N of block 428), theoptimization procedure 330 is terminated, and the current time schedule160 is updated with the time schedule corresponding to the solution x inthe search-space which is the global best solution in the swarm BG*(block 432).

The deadline time DT is set in such a way to allow the operationscorresponding to blocks from 404 to 426 that are carried out on all theM particles P(j) of the swarm to be repeated for a substantial highnumber of new swarm arrangements. Generally, the higher the deadlinetime DT, the lower the cost function corresponding to the global bestsolution in the swarm BG*. However, a long deadline time DT may reducethe time schedule 160 updating responsiveness of the system 100 topossible occurring events.

FIG. 5 illustrates in terms of functional blocks the swarm flightprocedure 430 according to an embodiment of the present invention.

As previously mentioned, each particle P(j) of the swarm corresponds toa complete solution, i.e., to a specific distribution in time of theenergy phases of the power profiles of the appliances 110(i). Since theswarm flight procedure 430 applies to the energy phase level, eachparticle P(j) of the swarm is made to correspond to a plurality of powerprofile sub-particles, each one corresponding in turn to a respectiveplurality of energy phase sub-particles, wherein each power profilesub-particle corresponds to a specific power profile and each energyphase sub-particle corresponds to a specific energy phase of such powerprofile.

The swarm flight procedure 430 provides that the local control unit 130carries out a same sequence of operations for each power profile of thesolution corresponding to a generic particle P(j), starting from a firstpower profile (block 502), and terminating when reaching the last powerprofile of the particle P(j).

Moreover, the local control unit 130 carries out a same sequence ofoperations for each energy phase sub-particle EPSP(k) (k=1, 2, . . . )of the generic power profile, starting from the first energy phasesub-particle EPSP(k) corresponding to the first energy phase of thepower profile, and terminating when reaching the last energy phasesub-particle EPSP(k) corresponding to the last energy phase of the powerprofile.

If the energy phase sub-particle EPSP(k) corresponds to the first energyphase of the power profile (exit branch Y of block 505), the localcontrol unit 130 sets the maximum delay for this energy phase to themaximum slack interval of the whole power profile (block 510), which isset by the user. If instead the energy phase sub-particle EPSP(k)corresponds to an energy phase of the power profile different from thefirst (exit branch N of block 505), the local control unit 130 sets themaximum delay for this energy phase to the minimum between its maximumdelay imposed by the power profile specification and the remainingcurrent slack interval of the power profile (block 515).

After having calculated the maximum delay for the energy phase (eitherblock 510 or block 515), the local control unit 130 calculates acandidate new position x′(k) for the energy phase sub-particle EPSP(k)(block 520). More in detail, according to an embodiment of the presentinvention the local control unit 130 calculates a corresponding personalattractor position a(k) as shown in the following equation:

a(k)=r·p(k)+(1−r)·g*(k),

wherein r is a random number with uniform distribution in [0,1], p(k) isthe local best position so far taken by the energy phase sub-particleEPSP(k), which is determined in turn by the local best solution B(j)* sofar taken by the particle P(j), and g*(k) is the global best position ofthe swarm so far taken by the energy phase sub-particle, which isdetermined in turn by the global best solution in the swarm BG*. Then,the candidate new position x′(k) for the energy phase sub-particleEPSP(k) is set to:

x′(k)=a(k)+b·(a(k)−x(k))·c,

wherein b is a constant, the constriction coefficient, which controlsthe step size of the jump from the previous position x(k) of the energyphase sub-particle EPSP(k), and c is a random number with Lévydistribution.

At this point, the local control unit 130 checks if the candidate newposition x′(k) corresponds to a delay for the energy phase sub-particleEPSP(k) that is higher than the maximum delay for the correspondingenergy phase (block 525).

In the affirmative case (exit branch Y of block 525), the candidate newposition x′(k) is discarded (block 530), and the energy phasesub-particle EPSP(k) is not moved, while in the negative case (exitbranch N of block 525) is actually moved to the candidate new positionx′(k) (block 535).

Then, the slack interval for the energy phase sub-particle EPSP(k) isupdated taking into account the new position (block 540).

Afterward, a check is made (block 545) to determine whether the energyphase sub-particle EPSP(k) is the last one of the power profile or not.If the energy phase sub-particle EPSP(k) is not the last one (exitbranch N of block 545), the previously described operation sequence isreiterated on the next energy phase sub-particle EPSP(k+1) of the powerprofile (block 550, then return back to block 505). If instead theenergy phase sub-particle EPSP(k) is the last one (exit branch N ofblock 545), it means that all the energy phases of the power profilehave been scheduled.

In this case, the local control unit 130 checks whether the powerprofile is the last one of the particle P(j) or not.

If the power profile is not the last one (exit branch N of block 550),the local control unit 130 selects a next power profile of the particleP(j) (block 555) and reiterates the operation previously described forthe energy phase sub-particles EPSP(k) of the new power profile (returnto block 505).

If instead the power profile is the last power profile of the particleP(j) (exit branch Y of block 550), the procedure for that particle P(j)is completed.

The swarm flight procedure 430 is terminated when all the energy phasesub-particles EPSP(k) of all the power profiles of all the particlesP(j) of the swarm have been processed, altering the swarm arrangement.

FIG. 6 illustrates in terms of functional blocks the reactivesub-procedure 250 of the power managing procedure 200 illustrated inFIG. 2 according to an embodiment of the present invention. Purpose ofthe reactive sub-procedure 250 is to reduce the power consumption and/ordeactivate the currently active appliances 110(i) in case the overallelectric power actually drained by the presently active appliances110(i) is higher than the guard threshold GTH.

Firstly, the local control unit 130 checks if among the currently activeappliances 110(i) there is at least one appliance 110(i) that can beregulated so as to reduce its power consumption (block 605).

In the affirmative case (exit branch Y of block 605), the local controlunit 130 checks whether the user of the system 100 has set a prioritylist of the appliances 110(i) or not (block 610).

In the affirmative case (exit branch Y of block 610), the local controlunit 130 starts a priority-based dimming procedure (block 615) whichprovides for sequentially driving the appliances 110(i) in such a way toreduce their power consumptions based on the priority list. In detail,the control unit 130 drives the first appliance 110(i) listed in thepriority list for reducing its current power consumption. Then, if theoverall electric power actually drained falls below the guard thresholdGTH, the priority-based dimming procedure 615 is terminated, otherwisethe control unit 130 drives the power reduction of the second appliance110(i) of the priority list. The priority-based dimming procedure 615 isreiterated until the overall electric power actually drained falls belowthan the guard threshold GTH, or until all the appliances 110(i) listedin the priority list have been driven to reduce their power consumption.

In case the priority list is not present (exit branch N of block 605),the local control unit 130 starts a consumption-based dimming procedure(block 620) which provides for sequentially driving the appliances110(i) starting from the ones which consume more. In detail, the controlunit 130 drives the appliance 110(i) which currently has the highestpower consumption so as to reduce it. Then, if the overall electricpower actually drained falls below the guard threshold GTH, theconsumption-based dimming procedure 620 is terminated, otherwise thecontrol unit 130 drives the power reduction of the appliance 110(i)currently having the second highest power consumption. Theconsumption-based dimming procedure 620 is reiterated until the overallelectric power actually drained falls below than the guard thresholdGTH, or until all the appliances 110(i) have been driven to reduce theirpower consumption.

In any case, at the end of the priority-based dimming procedure (block615) or at the end of the consumption-based dimming procedure (block620), a further check is made for assessing whether the overall electricpower actually drained falls below the guard threshold GTH (block 625)or not.

In the affirmative case (exit branch Y of block 625), the reactivesub-procedure 250 is terminated (returning back to block 210 of thepower managing procedure 200 of FIG. 2).

In the negative case (exit branch N of block 625), or in case the localcontrol unit 130 has assessed that among the currently active appliances110(i) there are not appliances 110(i) that can be regulated so as toreduce their power consumption (exit branch N of block 605), the localcontrol unit 130 reduces the overall electric power consumption byturning off one or more appliance 110(i).

More in detail, the local control unit 130 checks whether the user ofthe system 100 has set a priority list of the appliances 110(i) or not(block 630).

In the affirmative case (exit branch Y of block 630), the local controlunit 130 starts a priority-based turning off procedure (block 635) whichprovides for sequentially turning off the appliances 110(i) based on thepriority list. In detail, the control unit 130 turns off the firstappliance 110(i) listed in the priority list. Then, if the overallelectric power actually drained falls below the guard threshold GTH, thepriority-based turning off procedure 635 is terminated, otherwise thecontrol unit 130 turns off the second appliance 110(i) of the prioritylist. The priority-based turning off procedure 635 is reiterated untilthe overall electric power actually drained falls below than the guardthreshold GTH.

In case the priority list is not present (exit branch N of block 630),the local control unit 130 starts a consumption-based turning offprocedure (block 640) which provides for sequentially turning off theappliances 110(i) starting from the ones which consume more. In detail,the control unit 130 turns off the appliance 110(i) which currently hasthe highest power consumption. Then, if the overall electric poweractually drained falls below the guard threshold GTH, theconsumption-based turning off procedure 640 is terminated, otherwise thecontrol unit 130 turns off the appliance 110(i) currently having thesecond highest power consumption. The consumption-based turning offprocedure 640 is reiterated until the overall electric power actuallydrained falls below the guard threshold GTH.

In any case, at the end of the priority-based turning off procedure(block 635) or at the end of the consumption-based turning off procedure(block 640), the reactive sub-procedure 250 is terminated (returningback to block 210 of the power managing procedure 200 of FIG. 2).

In order to improve the power management efficiency, according to anembodiment of the present invention the local control unit 130 isprovided with a forecaster module (not illustrated) adapted to collectdata regarding how the electric power consumption of the appliances110(i) in the domestic area 120 has evolved up to the present and carryout a statistical analysis on such collected data to predict futurepower consumptions, so as to influence the power managing procedure 200in such a way to minimize the occurrences of guard threshold GTHexceeding.

The previous description presents and discusses in detail severalembodiments of the present invention; nevertheless, several changes tothe described embodiments, as well as different invention embodimentsare possible, without departing from the scope defined by the appendedclaims.

1. A system for managing house appliances supplied through a power grid,each house appliance being adapted to operate according to at least onecorresponding operative mode, each operative mode comprising a sequenceof operative phases, the system comprising at least one control unitinterfaced with the house appliances for exchanging data, the at leastone control unit being configured to: collect power profile datacomprising timing and electric power consumption data of each operativephase of each operative mode of the house appliances; generate a timeschedule of the house appliances operations by distributing in timeexecution of the operative phases thereof in such a way that a totalpower consumption of the house appliances is kept under a maximum powerthreshold of the power grid; and control the operation of the appliancesbased on the time schedule, wherein the at least one control unit isconfigured to generate the time schedule by exploiting a Particle SwarmOptimization approach based on a nature-inspired evolutionary flight. 2.The system of claim 1, wherein the at least one control unit isconfigured to generate the time schedule based on a Quantum ParticleSwarm Optimization approach exploiting a Lévy probability distribution.3. The system of claim 2, wherein the at least one control unit isconfigured to generate the time schedule by: randomly generating apopulation of candidate time schedules within a search space comprisingall possible candidate time schedules; for each candidate time scheduleof the population, calculating a corresponding cost by using a costfunction; moving the candidate time schedules in the search space basedon the cost thereof with a Quantum Particle Swarm Optimization approachexploiting a Lévy probability distribution; and generating the timeschedule based on the candidate time schedules.
 4. The system of claim3, wherein the at least one control unit is configured to: select acandidate time schedule global best position in the search space amongpositions previously taken by all the candidate time schedules in thesearch space based on the cost thereof, and for each candidate timeschedule, moving the candidate time schedule by: selecting a candidatetime schedule personal best position in the search space among positionspreviously taken by such candidate time schedule in the search spacebased on the cost thereof, and moving the candidate time schedule basedon the candidate time schedule global best position and based on itscandidate time schedule personal best position.
 5. The system of claim4, wherein the at least one control unit is configured to move thecandidate time schedule from a current position in the search space to anew position in the search space by: calculating a personal attractorposition in the search space based on the candidate time schedulepersonal best position and based on the candidate time schedule globalbest position, and setting the new position in the search space based onthe personal attractor position and based on a difference between: a)the personal attractor position, and b) the current position, multipliedby a random number with Lévy distribution.
 6. The system of claim 3,wherein the cost function of a candidate time schedule depends on adifference between the maximum power threshold of the power grid and apower consumption of the house appliances that would occur if the houseappliances operated following the candidate time schedule.
 7. The systemof claim 6, wherein the cost function of a candidate time schedulefurther depends on user preferences and/or on constraints imposed by theoperative modes of the house appliances.
 8. The system of claim 1,wherein the at least one control unit is further configured to controlthe house appliances in order to reduce power consumption of currentlyactive house appliances and/or to deactivate at least one of thecurrently active house appliances if the power consumption of thecurrently active house appliances is higher than a guard threshold lowerthan the maximum power threshold of the power grid.
 9. The system ofclaim 8, wherein the at least one control unit is configured tosequentially drive the currently active house appliances to reduce thepower consumption thereof and/or to sequentially deactivate thecurrently active house appliances according to a priority list definedby a user and/or starting from the currently active house appliancesconsuming more power, until the power consumption of the currentlyactive house appliances falls below the guard threshold.
 10. The systemof claim 1, wherein the house appliances are located in a domestic area,and the at least one control unit comprises a local control unit locatedin the domestic area wirelessly interfaced with the house appliance forexchanging data.
 11. The system of claim 10, wherein the at least onecontrol unit further comprises at least one remote control unit remotelyconnected with the local control unit, and wherein: the at least oneremote control unit is configured to generate at least one remotelygenerated time schedule, and the local control unit is configured togenerate a locally generated time schedule, the time schedule beingselected between the at least one remotely generated time schedule andthe locally generated time schedule based on costs of the at least oneremotely generated time schedule and of the locally generated timeschedule.
 12. A method for managing house appliances supplied through apower grid, each house appliance being adapted to operate according toat least one corresponding operative mode, each operative modecomprising a sequence of operative phases, the method comprising:collecting power profile data comprising timing and electric powerconsumption data of each operative phase of each operative mode of thehouse appliances; generating a time schedule of house appliancesoperations by distributing in time execution of the operative phasesthereof in such a way that a total power consumption of the houseappliances is kept under a maximum power threshold of the power grid;and controlling the operation of the house appliances based on the timeschedule, wherein the step of generating the time schedule includesexploiting a Particle Swarm Optimization approach based on anature-inspired evolutionary flight.
 13. The method of claim 12, whereinthe step of generating the time schedule is based on a Quantum ParticleSwarm Optimization approach exploiting a Lévy probability distribution.14. The method of claim 13, wherein the generating the time schedulecomprises: randomly generating a population of candidate time scheduleswithin a search space comprising all possible candidate time schedules;for each candidate time schedule of the population, calculating acorresponding cost by using a cost function; moving the candidate timeschedules in the search space based on the cost thereof with a QuantumParticle Swarm Optimization approach exploiting a Lévy probabilitydistribution; and generating the time schedule based on the candidatetime schedules.
 15. The method of claim 14, further comprising:selecting a candidate time schedule global best position in the searchspace among positions previously taken by all the candidate timeschedules in the search space based on the cost thereof, and for eachcandidate time schedule, moving the candidate time schedule by:selecting a candidate time schedule personal best position in the searchspace among positions previously taken by such candidate time schedulein the search space based on the cost thereof, and moving the candidatetime schedule based on the candidate time schedule global best positionand based on its candidate time schedule personal best position.
 16. Themethod of claim 15, wherein the moving the candidate time schedule froma current position in the search space to a new position in the searchspace comprises: calculating a personal attractor position in the searchspace based on the candidate time schedule personal best position andbased on the candidate time schedule global best position, and settingthe new position in the search space based on the personal attractorposition and based on a difference between: a) the personal attractorposition, and b) the current position, multiplied by a random numberwith Lévy distribution.
 17. A computer readable medium having a softwareprogram stored thereon that, when executed by a processor, causes theprocessor to perform operations comprising: collecting power profiledata comprising timing and electric power consumption data of eachoperative phase of each operative mode of house appliances; generating atime schedule of operations of the house appliances by distributing intime the execution of the operative phases thereof in such a way that atotal power consumption of the house appliances is kept under a maximumpower threshold of the power grid; and controlling the operation of thehouse appliances based on the time schedule, wherein the generating thetime schedule includes exploiting a Particle Swarm Optimization approachbased on a nature-inspired evolutionary flight.
 18. The computerreadable medium according to claim 17, wherein the generating the timeschedule comprises: randomly generating a population of candidate timeschedules within a search space comprising all possible candidate timeschedules; for each candidate time schedule of the population,calculating a corresponding cost by using a cost function; moving thecandidate time schedules in the search space based on the cost thereofwith a Quantum Particle Swarm Optimization approach exploiting a Lévyprobability distribution; and generating the time schedule based on thecandidate time schedules.
 19. The computer readable medium according toclaim 18, wherein the software program, which executed by the processor,further causes the processor to perform operation further comprising:selecting a candidate time schedule global best position in the searchspace among positions previously taken by all the candidate timeschedules in the search space based on the cost thereof, and for eachcandidate time schedule, moving the candidate time schedule by:selecting a candidate time schedule personal best position in the searchspace among positions previously taken by such candidate time schedulein the search space based on the cost thereof, and moving the candidatetime schedule based on the candidate time schedule global best positionand based on its candidate time schedule personal best position.
 20. Thecomputer readable medium according to claim 19, wherein the moving thecandidate time schedule from a current position in the search space to anew position in the search space comprises: calculating a personalattractor position in the search space based on the candidate timeschedule personal best position and based on the candidate time scheduleglobal best position, and setting the new position in the search spacebased on the personal attractor position and based on a differencebetween: a) the personal attractor position, and b) the currentposition, multiplied by a random number with Lévy distribution.